Differential Evolution versus Genetic Algorithms in Multiobjective Optimization
نویسندگان
چکیده
This paper presents a comprehensive comparison between the performance of state-of-the-art genetic algorithms NSGA-II, SPEA2 and IBEA and their differential evolution based variants DEMO, DEMO and DEMO. Experimental results on 16 numerical multiobjective test problems show that on the majority of problems, the algorithms based on differential evolution perform significantly better than the corresponding genetic algorithms with regard to applied quality indicators. This suggests that in numerical multiobjective optimization, differential evolution explores the decision space more efficiently than genetic algorithms.
منابع مشابه
Xergy analysis and multiobjective optimization of a biomass gasification-based multigeneration system
Biomass gasification is the process of converting biomass into a combustible gas suitable for use in boilers, engines, and turbines to produce combined cooling, heat, and power. This paper presents a detailed model of a biomass gasification system and designs a multigeneration energy system that uses the biomass gasification process for generating combined cooling, heat, and electricity. Energy...
متن کاملMultiobjective Differential Evolution with Application to Reservoir System Optimization
Many water resources systems are characterized by multiple objectives. For multiobjective optimization, typically there can be no single optimal solution which can simultaneously satisfy all the goals, but rather a set of technologically efficient noninferior or Pareto optimal solutions exists. Generating those Pareto optimal solutions is a challenging task and often difficulties arise in using...
متن کاملDesign of an Algorithm for Multiobjective Optimization with Differential Evolution
In many real-world optimization problems the quality of solutions is determined with several fundamentally different objectives, such as, for example, cost, performance and profit. These objectives are often mutually conflicting thus yielding several optimal solutions, where each of them represents a different tradeoff between the objectives. Classicalmethods solve multiobjective optimization p...
متن کاملWell Placement Optimization Using Differential Evolution Algorithm
Determining the optimal location of wells with the aid of an automated search algorithm is a significant and difficult step in the reservoir development process. It is a computationally intensive task due to the large number of simulation runs required. Therefore,the key issue to such automatic optimization is development of algorithms that can find acceptable solutions with a minimum numbe...
متن کاملA Review towards Evolutionary Multiobjective optimization Algorithms
Multi objective optimization is a promising field which is increasingly being encountered in many areas worldwide. Various metaheuristic techniques such as differential evolution (DE), genetic algorithm (GA), gravitational search algorithm (GSA), and particle swarm optimization (PSO) have been used to solve Multi objective problems. Various multiobjective evolutionary algorithms have been devel...
متن کامل